I tried to apply PCA to my data and then apply RandomForest to the transformed data. However, PCA.transform(data) gave me a DataFrame but I need a mllib LabeledPoints to feed my RandomForest. How can I do that? My code:
import org.apache.spark.mllib.util.MLUtils
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.mllib.tree.RandomForest
import org.apache.spark.mllib.tree.model.RandomForestModel
import org.apache.spark.ml.feature.PCA
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.linalg.Vectors
val dataset = MLUtils.loadLibSVMFile(sc, "data/mnist/mnist.bz2")
val splits = dataset.randomSplit(Array(0.7, 0.3))
val (trainingData, testData) = (splits(0), splits(1))
val trainingDf = trainingData.toDF()
val pca = new PCA()
.setInputCol("features")
.setOutputCol("pcaFeatures")
.setK(100)
.fit(trainingDf)
val pcaTrainingData = pca.transform(trainingDf)
val numClasses = 10
val categoricalFeaturesInfo = Map[Int, Int]()
val numTrees = 10 // Use more in practice.
val featureSubsetStrategy = "auto" // Let the algorithm choose.
val impurity = "gini"
val maxDepth = 20
val maxBins = 32
val model = RandomForest.trainClassifier(pcaTrainingData, numClasses, categoricalFeaturesInfo,
numTrees, featureSubsetStrategy, impurity, maxDepth, maxBins)
error: type mismatch;
found : org.apache.spark.sql.DataFrame
required: org.apache.spark.rdd.RDD[org.apache.spark.mllib.regression.LabeledPoint]
I tried the following two possible solutions but they didn't work:
scala> val pcaTrainingData = trainingData.map(p => p.copy(features = pca.transform(p.features)))
<console>:39: error: overloaded method value transform with alternatives:
(dataset: org.apache.spark.sql.DataFrame)org.apache.spark.sql.DataFrame <and>
(dataset: org.apache.spark.sql.DataFrame,paramMap: org.apache.spark.ml.param.ParamMap)org.apache.spark.sql.DataFrame <and>
(dataset: org.apache.spark.sql.DataFrame,firstParamPair: org.apache.spark.ml.param.ParamPair[_],otherParamPairs: org.apache.spark.ml.param.ParamPair[_]*)org.apache.spark.sql.DataFrame
cannot be applied to (org.apache.spark.mllib.linalg.Vector)
And:
val labeled = pca
.transform(trainingDf)
.map(row => LabeledPoint(row.getDouble(0), row(4).asInstanceOf[Vector[Int]]))
error: type mismatch;
found : scala.collection.immutable.Vector[Int]
required: org.apache.spark.mllib.linalg.Vector
(I have imported org.apache.spark.mllib.linalg.Vectors in the above case)
Any help?
The correct approach here is the second one you tried - mapping each Row
into a LabeledPoint
to get an RDD[LabeledPoint]
. However, it has two mistakes:
Vector
class (org.apache.spark.mllib.linalg.Vector
) does NOT take type arguments (e.g. Vector[Int]
) - so even though you had the right import, the compiler concluded that you meant scala.collection.immutable.Vector
which DOES.The DataFrame returned from PCA.fit()
has 3 columns, and you tried to extract column number 4. For example, showing first 4 lines:
+-----+--------------------+--------------------+
|label| features| pcaFeatures|
+-----+--------------------+--------------------+
| 5.0|(780,[152,153,154...|[880.071111851977...|
| 1.0|(780,[158,159,160...|[-41.473039034112...|
| 2.0|(780,[155,156,157...|[931.444898405036...|
| 1.0|(780,[124,125,126...|[25.5114585648411...|
+-----+--------------------+--------------------+
To make this easier - I prefer using the column names instead of their indices.
So here's the transformation you need:
val labeled = pca.transform(trainingDf).rdd.map(row => LabeledPoint(
row.getAs[Double]("label"),
row.getAs[org.apache.spark.mllib.linalg.Vector]("pcaFeatures")
))